Murrell, Paul (2009). Introduction to Data Technologies, London: Chapman & Hall/CRC.
20/09/2018
Data Science workflow. Source: Wickham and Grolemund (2017)
Data Science workflow. Source: Wickham and Grolemund (2017)
What could be the output of all this?
“This coupling of scientific discovery and practice involves the collection, management, processing, analysis, visualization, and interpretation of vast amounts of heterogeneous data associated with a diverse array of scientific, translational, and inter-disciplinary applications.”
University of Michigan ‘Data Science Initiative’, 2015
“Seemingly, statistics is being marginalized here; the implicit message is that statistics is a part of what goes on in data science but not a very big part. At the same time, many of the concrete descriptions of what the DSI will actually do will seem to statisticians to be bread-and-butter statistics. Statistics is apparently the word that dare not speak its name in connection with such an initiative!”
David Donoho (2015). 50 years of Data Science
The ‘Data Science Venn Diagram’. Source: http://berkeleysciencereview.com/how-to-become-a-data-scientist-before-you-graduate/
The ‘Data Science Venn Diagram’. Source: http://berkeleysciencereview.com/how-to-become-a-data-scientist-before-you-graduate/
“All in all, I have come to feel that my central interest is in data analysis, which I take to include, among other things: …”
“All in all, I have come to feel that my central interest is in data analysis, which I take to include, among other things: procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”
Source: https://techxerl.net.
Source: Source: statista.com.
Top: Number of mentions of the terms ‘Big Data’ or ‘Artificial Intelligence’ in academic and media sources, 1980-2016. Bottom: Number of mentions in The New York Times and The Wall Street Journal, used as proxies for U.S. mainstream media and business media. Note logarithmic y-axis scale. Source: Katz (2017).
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| Date | Topic |
|---|---|
| 20.09.2018 | Introduction: Big Data/Data Science, course overview |
| 27.09.2018 | An introduction to data and data processing |
| 27.09.2018 | Exercises/Workshop 1: Tools, working with text files |
| 04.10.2018 | Data storage and data structures |
| 11.10.2018 | ’Big Data‘ from the Web |
| 11.10.2018 | Exercises/Workshop 2: Computer code and data storage |
| Date | Topic |
|---|---|
| 18.10.2018 | Programming with data |
| 25.10.2018 | Data sources, data gathering, data import |
| 25.10.2018 | Exercises/Workshop 3: Programming with Data |
| 15.11.2018 | Guest Lecture: Dr. Michael Zehnder (Swiss Data Labs, gateB) |
| 22.11.2018 | Data preparation and manipulation |
| 22.11.2018 | Exercises/Workshop 4: Data import and data preparation/manipulation |
| 29.11.2018 | Research Insights: The Programmable Web, Big Public Data, and Political Economics |
| Date | Topic |
|---|---|
| 06.12.2018 | Basic statistics and data analysis with R |
| 06.12.2018 | Exercises/Workshop 5: Applied data analysis with R |
| 13.12.2018 | Visualization, dynamic documents |
| 20.12.2018 | Exercises/Workshop 6: Visualization, dynamic documents; Wrap-Up, Q&A |
| 20.12.2018 | Exam Exchange Students |
Murrell, Paul (2009). Introduction to Data Technologies, London: Chapman & Hall/CRC.
Katz, Yarden. 2017. “Manufacturing an Artificial Intelligence Revolution.” https://ssrn.com/abstract=3078224.
Wickham, Hadley, and Garrett Grolemund. 2017. Sebastopol, CA: O’Reilly. http://r4ds.had.co.nz/.